Innovation Activities and Integration through
Vertical Acquisitions
Laurent Frésard
Universita della Svizzera italiana (USI), Swiss Finance Institute
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Gerard Hoberg
Marshall School of Business, University of Southern California
Gordon M. Phillips
Tuck School at Dartmouth College and National Bureau of Economic
Research
We examine the determinants of vertical acquisitions using product text linked to product
vocabulary from input-output tables. We find that the innovation stage is important in
understanding vertical integration. R&D-intensive firms are less likely to become targets
of vertical acquisitions. In contrast, firms with patented innovation are more likely to sell
to vertically related buyers. Firms’ R&D intensity is a more important deterrent to their
vertical acquisitions when the provision of innovation incentives by potential acquirers is
more difficult. The role of patents in fostering vertical acquisitions is more prevalent when
potential buyers face a higher risk of holdup. (JEL G32, G34, L22, L25, O34)
Received December 20, 2017; editorial decision July 7, 2019 by Editor Francesca Cornelli.
The scope of firm boundaries and whether to organize transactions within
the firm (integration) or by using external contracting is of major interest in
We are especially grateful to Francesca Cornelli (the editor) and two anonymous referees for extensive and
very thoughtful suggestions. We thank Yun Ling for excellent research assistance. For helpful comments, we
thank Kenneth Ahern, Jean-Noel Barrot, Thomas Bates, Nick Bloom, Giacinta Cestone, Francesca Cornelli,
Robert Gibbons, Oliver Hart, Thomas Hellmann, Ali Hortaçsu, Adrien Matray, Sébastien Michenaud, Chad
Syverson, Steve Tadelis, and Ivo Welch and seminar participants at the National Bureau of Economic Research
Organizational Economics Meetings, 2015 International Society for New Institutional Economics Meetings,
the American Finance Association Meetings, Arizona State University, Berkeley, Carnegie Mellon, Dartmouth
College, Humboldt University, IFN Stockholm, Harvard-MIT Organizational Economics joint seminar, Tsinghua
University, University of Alberta, University of British Columbia, University of California Los Angeles,
University of Chicago, Universidad de los Andes, University of Maryland, University of Washington, VU
Amsterdam, and Wharton. All authors contributed equally to this paper. Supplementary data can be found
on The Review of Financial Studies web site. Send correspondence to Gerard Hoberg, Marshall School of
Business, 701 Exposition Blvd, Hoffman Hall 231, Los Angeles, CA 90089; telephone: 213-740-2348. E-mail:
hoberg@marshall.usc.edu.
© The Author(s) 2019. Published by Oxford University Press on behalf of The Society for Financial Studies.
All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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understanding why firms exist. A large literature investigates the determinants
of vertical integration and, more recently, the relationship between firms’
vertical organization and innovation activities.1 Yet, existing research remains
largely silent on the role of firms’ innovation activities in explaining vertical
acquisitions. In this paper, we provide novel evidence that the stage of
development of innovative assets is a strong determinant of firms’ vertical
acquisitions and the boundaries of the firm.
Our analysis builds on the theories of the firm of Williamson (1971),
Grossman and Hart (1986), and Hart and Moore (1990) who emphasize that,
because of incomplete contracting and the inalienability of human capital, the
vertical boundaries between firms (separation or integration) are determined
by trading off their ex ante investment incentives and ex post opportunistic
behaviors. This trade-off is regulated by the allocation of control rights over the
assets that are specific to firms’ vertical relationships, as the firms that formally
control these assets have more incentives to invest ex ante due to increased
bargaining power ex post. Therefore, change in control – vertical acquisitions
– should occur when the relative importance of each party’s incentives to add
value to the overall relationship shifts.2
Early stage innovation activities (i.e., unrealized innovation) are particularly
sensitive to the allocation of control rights because investments in innovation
are typically not fully contractible, often unverifiable, heavily depend on human
capital, and are subject to holdup because of their specificities (e.g., Acemoglu
1996). When innovative assets require further investment and development, it
is optimal to leave control to the firms that perform the innovation, as their
incentives are most important for the value of the vertical relationship (e.g.,
Aghion and Tirole 1994), and because their employees may leave in case
of acquisition and take the unrealized innovation (i.e., their ideas) with them
(e.g., Hart and Moore 1994).3 Vertical separation preserves incentives for the
innovator (Grossman and Hart 1986). In contrast, when innovation is realized,
and protected by legally enforceable patents, incentives to invest in innovation
decline in importance and incentives to commercialize the innovation increase.
In this context, vertical acquisitions optimally reallocate control to the firms that
commercialize the innovation and thus integration reduces the risk of holdup by
the other party (Williamson 1971, 1979; Klein, Crawford, and Alchian 1978).
We thus predict that acquisitions by vertically related buyers are less likely
when firms’ innovation is still unrealized, but more likely when it is realized
and patented.
1 See Lafontaine and Slade (2007) and Bresnahan and Levin (2012) for recent surveys.
2 Holmstrom and Milgrom (1991) and Holmstrom and Milgrom (1994) also emphasize the role of incentives
in firm structure. Gibbons (2005) summarizes the large literature and highlights that the costs and benefits of
vertical integration depend on transactions costs, rent seeking, contractual incompleteness, and the specificity of
the assets involved in transactions.
3 For instance, the original innovating team could freely form a new firm and use the unrealized innovation to
compete against the purchasing firm.
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To test this hypothesis, we construct a novel measure of vertical relatedness
between firm-pairs by linking product vocabularies from the Bureau of
Economic Analysis (BEA) input-output tables to firms’ 10-K product
descriptions filed with the Securities and Exchange Commission (SEC).
Because 10-Ks are updated annually, the result is a dynamic directed network of
vertical relatedness between all publicly traded firms between 1996 and 2013. In
this text-based network, we identify for instance that Tesla is vertically related
(downstream) to Honeywell, Goodyear, and Sun Hydraulics in 2010. This
pairwise network allows us to identify precisely which mergers and acquisitions
occur between firms offering vertically related products. We use the same
approach to also measure the degree of firms’ vertical integration based on
whether firms use product vocabulary that spans vertically related markets.
Using a large sample of more than 8,000 firms, we find a strong association
between the stage of development of firms’ innovation and vertical acquisitions.
Our main tests focus on targets in vertical deals since they are the party that
relinquishes control rights by selling, and for which the trade-off between ex
ante investment incentives and holdup is important. Consistent with vertical
separation being optimal when innovation is unrealized, we find that R&Dintensive firms are significantly less likely to be acquired in vertical transactions.
In contrast, when innovation is realized and protected by legally enforceable
patents, firms are more likely be acquired by a vertical buyer. The positive link
between firms’ patenting intensity and their vertical acquisitions is consistent
with patenting marking a shift away from incentives for further R&D and toward
incentives to commercialize the innovation.
The opposite roles of unrealized and realized innovation in vertical
acquisitions are highly robust across a host of alternative specifications,
including alternative measures of firms’ innovative efficiency and strict controls
for the potential multicollinearity between firms’ R&D and patenting intensity.
The estimated relations are also economically important as a 1-standarddeviation increase in firms’ R&D intensity is associated with a large 0.46%
percentage point reduction in their probability of being acquired in a vertical
transaction. This decrease represents roughly one-third of the unconditional
probability of being acquired by a vertical buyer. The economic magnitude of
firms’ patenting intensity in explaining vertical acquisitions are even larger.
Western Digital’s purchase of Komag in 2007 illustrates the logic of our
results. Komag specialized in developing thin-file disks of the sort used in hard
drives. Buying Komag transferred control of Komag’s realized innovation to
Western Digital, thus reducing ex post holdup and increasing Western Digital’s
commercialization incentives. Western Digital’s CEO John Coyne commented
that “the acquisition of Komag will enable Western Digital to optimize synergies
through the integration of heads and media” (Duncan 2007). Another example
is GE’s acquisition of Edwards Systems Technology in 2004, which gave GE
control of Edward’s fire detection technology, used in GE’s broad security
offering. Will Woodburn, CEO of GE Infrastructure, indicated that Edwards’
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technology will enable GE to offer an “integrated facility management solution
to partners and end users” (Buildings 2004).
Our hypothesis specifically points to the trade-off between maintaining
innovation incentives and limiting holdup risk. We thus examine whether the
negative relation between R&D and acquisition, and the positive association
between patenting and acquisition, is stronger when these trade-offs are most
salient. First, we show that the negative link between firms’ R&D intensity and
vertical acquisitions is significantly stronger when potential vertical buyers
likely find it difficult to maintain the innovation incentives of the target
and keep its key employees post-acquisition, as measured using inventor
mobility and the intensity of each firm’s 10-K disclosures relating to employee
incentives.4 Second, we show that the positive link between firms’ patenting
intensity and vertical acquisitions is significantly stronger when opportunistic
behaviors are more likely, which we measure using patent infringement
litigation, poor secondary market liquidity, and high product market
concentration.
Our tests are consistent with the idea that investment incentives and holdup
risk increase the sensitivity of vertical boundaries to the stage of innovation.
Nevertheless, our tests do not fully rule out some alternative explanations and
we further test two specific alternatives. First, our results could be driven
by potential vertical buyers relying on patent grants as signals of a target’s
innovation efficiency. Second, our findings might reflect firms responding
to the likelihood of vertical acquisitions by reducing their R&D intensity
and increasing their patenting intensity (reverse causality) to attract a better
offer. Several tests mitigate both concerns. For instance, inconsistent with
patent grants signaling innovative efficiency, our results are robust to including
controls for firms’ innovative efficiency (patents by dollar of R&D). We further
examine the timing of vertical acquisitions and find that patenting intensity
increases around the acquisition. The drop in firms’ R&D intensity occurs
mostly after control is transferred to the buyers, which is inconsistent with a
reverse causality explanation.
Inconsistent with many other alternatives, we show that our results are
unique to vertical acquisitions as we find opposite or insignificant results for
nonvertical acquisitions (in line with Bena and Li 2014). We recognize that part
of our results might arise because some acquirers might use patent grants as
an indication of the degree of the target’s success, triggering their acquisition.
This channel can be distinct from our explanation if no ex ante incentives
are required for the effort needed to achieve that degree of success. Yet, our
4 Note we do not study “acqui-hires,” where firms acquire a company to recruit its employees without necessarily
showing an interest in its current products and services. In many such cases, the product that these previously
separate employees were working on either has failed or is not significant enough to develop within the firm.
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subsample tests based on proxies for incentive provision and holdup risk provide
specific support for our proposed incentive-based channel.
Finally, we show that our new measures of the degree of firms’ vertical
integration are also consistently related to the stage of innovation development.
In particular, firms operating in industries characterized by high levels of
R&D intensity display lower levels of vertical integration. In contrast, firms in
patenting intensive industries appear significantly more integrated vertically, in
line with vertical integration being more prevalent in sectors where innovation is
realized. Using within firm analysis, we uncover a positive relationship between
vertical integration and firms’ patenting intensity over time, but only for firms
that have been vertical acquirers. In contrast, changes in firms’ degree of vertical
integration is largely unrelated to their R&D intensity. These results support
our central hypothesis as these firms are specialized (i.e., nonintegrated) and
we predict and find that they tend to exit the sample via vertical acquisitions
once their innovation becomes realized.
Our findings contribute to the large literature examining the determinants
of firms’ vertical boundaries, and in particular to recent papers linking
vertical integration to innovation and intangible assets. Acemoglu et al. (2010)
show, in a sample of UK manufacturing firms, that backward integration
is positively (negatively) related to the R&D intensity of the downstream
(upstream) industry. Using Census data, Atalay, Hortacsu, and Syverson (2014)
report limited physical shipments within vertically integrated firms in the
United States, suggesting that innovation and intangible capital (which do
not require shipments) might be responsible for firms’ vertical organization.5
By showing that firms’ vertical boundaries are related to the stage of
innovative development, our paper provides direct evidence of the importance
of innovation for firms’ vertical boundaries.
Our paper also adds to the literature on the determinants and implications
of vertical acquisitions. Fan and Goyal (2006) and Kedia, Ravid, and Pons
(2011) examine stock market reactions to vertical deals, and Ahern (2012)
shows that the division of stock market gains is determined in part by customer
or supplier bargaining power. Ahern and Harford (2014) examine how supply
chain shocks translate into vertical merger waves. Our study is distinct and
focuses on the stage of innovation and a novel hypothesis linking this to the
choice of firms’ organizational form. Our results are consistent with the view
that vertical acquisitions emerge as an optimal way to transfer the residual
rights of control of relationship-specific intangible assets to the party whose
investment incentives are most important for the success of the relationship,
thus also reducing holdup risk. This motive is distinct from existing acquisition
5 The authors document a decline in nonproduction workers in vertically acquired establishments. They also find
a shift in production of the acquirer’s products toward the acquired firms’ establishments.
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motives emphasized in existing research including neoclassical theories, agency
theories, and horizontal theories.6
Our focus is on the motives for acquisitions of vertically related firms
who have actual products (i.e., publicly listed firms), and we do not directly
consider licensing or patent purchases, as they are outside the scope of
our study.7 We conjecture that licensing occurs when follow-on integration
or tailoring of the technology to a particular use after contracting is less
important. Patent sales occur when the scope of the patent is well defined
and the acquirer does not need resources or employees from the target to help
integrate the product or further develop the technology. Our hypothesis differs
from just purchasing patents or licensing because acquisitions among more
complex publicly traded firms typically involves additional needs beyond the
associated patents alone. For example, many innovation-driven acquisitions
require continued participation by the targets’ employees either regarding future
technological development or in adapting the technology to the purchaser.8
Thus, we expect to observe technology-driven vertical acquisitions despite the
possibility of patent purchases or licensing.
A key methodological contribution is that we can identify vertical relatedness
at the detailed firm-pair level. Existing measures are based on static NAICS
industry classifications and not only fail to provide firm-specific measures, but
are slow to update over time and are based on production processes and not the
products and services offered by firms.9 Our textual measures do not rely on the
quality of the Compustat segment tapes to delineate firms’ product markets, the
quality of the NAICS classification, or the links between NAICS codes and the
Input-Output tables, which are based on BEA classifications. This methodology
for measuring vertical linkages between firms extends the work of Hoberg and
Phillips (2016), who focus on horizontal links using 10-K text. Our approach
thus contributes to the growing literature that uses text as data in finance and
economics, recently surveyed by Gentzkow, Kelly, and Taddy (forthcoming).
1. Vertical Acquisitions and the Stage of Innovation
In this section, we discuss a framework to motivate our tests based on
the analysis of Grossman and Hart (1986), Aghion and Tirole (1994),
and Williamson (1971). As an illustration of the contrasting effects of
6 See Maksimovic and Phillips (2001), Jovanovic and Rousseau (2002), and Harford (2005) for neoclassical and
q theories; see Morck, Shleifer, and Vishny (1990) for an agency motivation for acquisitions; and see Phillips
and Zhdanov (2013) for a recent horizontal theory of acquisitions.
7 For studies in these areas, see Teece (1986) and Arora and Fosfuri (2003) for licensing and Serrano (2010) for
patents sales and transfers.
8 Apple, for example, purchased Intrinsity for its technology and the engineers who developed low power fast
chips at Intrinsity further enhanced Apple’s mobile and iPad chips.
9 The Federal Register states “NAICS was developed to classify units according to their production function.
NAICS results in industries that group units undertaking similar activities using similar resources but does not
necessarily group all similar products or outputs” (Federal Register 2014).
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realized and unrealized innovation on firms’ vertical boundaries, consider two
vertically related firms (e.g., upstream supplier and a downstream producer)
that cooperate to offer a product or service. These firms can operate as separate
entities or combine into a vertically integrated firm.10
The joint value of their vertical relationship and the price they
charge to consumers depends positively on R&D product investments and
commercialization investments, both of which are specific to the relationship.
Both upstream and downstream firms can conduct R&D. While both firms
conduct R&D, it is relatively more likely (but not a given as our examples later
show) that upstream firms conduct product R&D and downstream firms that
are closer to the end-consumer conduct investments and R&D that is useful
for commercialization. Either firm can have a differential mix of realized and
unrealized innovation.
As is standard in the literature (e.g., Aghion and Tirole 1994), the outcome
of the product innovation effort in each period is risky, succeeding only
probabilistically, and is noncontractible and unverifiable. The outcome of
innovation effort is protected by legally enforceable patents in case of success,
but remains intangible if unsuccessful. The commercialization investment in a
given period is less risky than the innovation effort, and occurs after its outcome
is realized. As the product (or service) resulting from firms’ relationship
matures over time, the marginal value of innovation effort to add new
technological features naturally decreases, whereas that of commercialization
effort marginally increases.
In our context, the optimal organizational form depends on the stage of
development of the innovation. First, consider the firms operating separately.
It is optimal for firms to stay separate when one firm’s product innovation
efforts have not succeeded yet and further innovation effort thus benefits
the relationship. In that case, separation optimally allocates control rights to
the party whose investment incentives are relatively more important. Indeed,
transferring control rights to the other firm when innovation is still unrealized
lowers innovation incentives, as innovative effort is not fully contractible.
In addition, integration does not guarantee the acquirer’s access to the other
firm’s unrealized innovation because of the absence of clearly defined property
rights. For instance, the value of unrealized innovation could be encapsulated in
employees’ knowledge, which is inalienable, as in Hart and Moore (1994), and
employees might freely leave in the case of integration. Intuitively, staying
separate is optimal when further incentives for R&D benefit the overall
relationship. In that case, individual ownership maintains ex ante incentives
for the firms to further invest. Separation optimally allocates residual rights of
control to the party who has unrealized innovation where incentives are more
important.
10 We included a simple model developing these ideas in an earlier version of this paper. This model is available
on the authors’ Web sites.
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Now consider the integration decision. At each point in time, the firms can
either operate as separate entities or decide to integrate through one party (either
upstream or downstream) buying the other.11 The party that sells its assets (the
target) loses control rights over the assets sold, and makes less new product
R&D investment, as this investment is noncontractible and the parties would
not be able to ensure payment ex post. The realization of R&D and the grants of
patents are thus key to determining whether firms integrate or remain separate.
When the innovation is successful and its features are protected by patents,
incentives for further innovation decline because of the decreasing marginal
value of innovation effort for the overall relationship. In contrast, the incentives
of the potential acquirer to invest in commercialization marginally increase.
However, the crucial tension is that without legal control rights on the outcome
of the realized innovation through the ownership of the patents, the user of the
innovation faces a risk of holdup, in the spirit of Williamson (1971), whereby
one firm can threaten to holdup the other firm after significant investment has
been made by both parties. The acquisition decision is made to encourage ex
post commercialization investments and to protect the two parties’ investments
in the relationship after more of the innovation becomes realized through
patents.
This holdup risk is likely in production when commercialization is more
important. Commercialization investments can include investments that lower
the cost of the product. Holdup risk can also include supply chain problems
that can persist when firms are separate and the quality of the engineers or
people involved in the production of the components supplied cannot be easily
contracted upon.12 To encourage commercialization incentives, the overall
relationship should optimally allocate the residual rights of control to one party
to reduce holdup costs. At this stage, vertical integration through acquisition is
optimal as it maximizes total surplus.
The trade-off between ex ante innovation incentives and ex post holdup risk
leads to our central prediction:
Central Prediction: Firms are less likely to be acquired by a vertically related
buyer when their innovation is unrealized. Firms are more likely to be acquired
by a vertically related buyer when their innovation is realized and protected by
patents.
11 Note that the overall framework is agnostic about whether the acquirer is the upstream firm or the downstream
firm closer to the customer. Several examples show that the acquirer can be either the upstream firm or the
downstream firm. Western Digital, a disk drive manufacturer, is an example of a downstream firm buying an
upstream firm. They purchased Komag Inc., a producer of thin-film discs used in hard drives. We also note
that Conexant, a semiconductor manufacturer, acquired an upstream firm, Globespan Virata Inc., a maker of
broadband communications products that use semiconductor chips. In both of these cases, the target was a high
R&D firm that, over time, realized more patents.
12 An example would be Boeing purchasing some of its previously separate suppliers and integrating them given
supply chain problems. For additional examples, see von Simson (2010).
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We test this prediction using new text-based measures of vertical relatedness
between firms, and by examining the impact of firms’ R&D and patenting
intensity on their likelihood of being purchased in vertical transactions.
2. Measuring Vertical Relatedness
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This section develops and validates our text-based measure of vertical
relatedness.
2.1 Data from 10-K business descriptions
We start with all firm-year observations in Compustat from 1996 to 2013 with
sales of at least $5 million and positive assets.13 We follow the same procedures
as Hoberg and Phillips (2016) to identify, extract, and parse 10-K annual firm
business descriptions from the SEC Edgar database. These 10-Ks are merged
with the Compustat-based sample using the central index key (CIK) mapping to
gvkey provided in the WRDS SEC Analytics package. Item 101 of Regulation
S-K requires business descriptions to accurately report (and update each year)
the significant products firms offer. We obtain 92,087 firm-years in the merged
Compustat/Edgar universe.
2.2 Data from input-output tables
We then use the 2002 BEA input-output (IO) tables, which provide dollar
flows between producers and purchasers in the U.S. economy (including
households, government, and foreign buyers of U.S. exports). The IO tables
are based on two primitives: “commodity” outputs (any good or service)
defined by the Commodity IO Code, and producing “industries” defined by the
Industry IO Code. In 2002, there were 424 commodities and 426 industries
in the “Make table,” which reports the dollar value of each commodity
produced by each industry. There are 431 commodities purchased by 439
industries or end users in the “Use table,” which reports the dollar value of
each commodity purchased by each industry.14 We use these IO tables to
compute three items: (1) commodity-to-commodity (upstream to downstream)
correspondence matrix (V ), (2) commodity-to-word correspondence matrix
(CW ), and (3) commodity-to-“exit” correspondence matrix (E).
In addition to the numerical values in the BEA data, we use an often
overlooked resource: the “Detailed Item Output” table, which verbally
13 We show in the Online Appendix that our results are robust to any minimum sales thresholds ranging from $1
million to $10 million.
14 An industry can produce more than one commodity: in 2002, the average (median) number of commodities
produced per industry was 18 (13). Industry output is also concentrated as the average commodity concentration
ratio is 0.78. Costs are reported in both purchaser and producer prices. We use producer prices. Seven commodities
in the Use table are not in the Make table, for example, compensation to employees. Thirteen “industries” in the
Use table are not in the Make table. These correspond to “end users” and include personal consumption, exports
and imports, and government expenditures.
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Table 1
BEA vocabulary example: Photographic and photocopying equipment
Description of commodity subcategory
Value of production ($Mil.)
266.1
72.4
40.5
266.5
592.4
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Still cameras (hand-type cameras, process cameras for photoengraving
and photolithography, and other still cameras)
Projectors
Still picture commercial-type processing equipment for film
All other still picture equipment, parts, attachments, and accessories
Photocopying equipment, including diffusion transfer, dye transfer,
electrostatic, light and heat sensitive types, etc.
Microfilming, blueprinting, and white-printing equipment
Motion picture equipment (all sizes 8 mm and greater)
Projection screens (for motion picture and/or still projection)
Motion picture processing equipment
20.7
149.0
204.9
23.0
Processed commodity vocabulary: microfilming, whiteprinting, blueprinting, interchangeable, film,
projectors, rear, viewers, screen, mm, photoengraving, photolithography, cameras, photographic, motion,
electrostatic, diffusion, dye, heat, projection, screens, picture, still, photocopying
This table provides an example of the BEA commodity “photographic and photocopying equipment” (IO
commodity code #333315). The table displays its subcommodities and their associated product text, along
with the value of production for each subcommodity.
describes each commodity and its subcommodities. The BEA also provides the
dollar value of each subcommodity’s total production and a commodity’s total
production is the sum of these subcommodity figures.15 Each subcommodity
textual description uses between 1 to 25 distinct words (the average is 8)
that summarizes the nature of the good or service provided. Table 1 contains
an example of product text corresponding to the BEA “photographic and
photocopying equipment” commodity (IO commodity code #333315). We label
the complete set of words associated with a commodity as “commodity words.”
We follow Hoberg and Phillips (2016) and only consider nouns and proper
nouns. We then apply four additional screens to ensure our identification of
vertical links is conservative. First, because commodity vocabularies identify
stand-alone product markets, we manually discard expressions that indicate
a vertical relation, such as “used in,” “made for,” or “sold to.” Second,
we remove expressions indicating exceptions (e.g, phrases beginning with
“except” or “excluding”). Third, we discard common words from commodity
vocabularies.16 Fourth and finally, we remove any words that do not frequently
coappear with the other words in a given commodity’s vocabulary. This
ensures that horizontal links are not mislabeled as vertical links. To do so,
we compute the fraction of times each focal word coappears with the same
peer words used in a given IO commodity when it appears in a 10-K business
description using all 10-Ks in 1997. For each commodity, we then discard
one-third of its words, specifically those in the bottom tercile by this measure
15 There were 5,459 subcommodities in 2002. The average number of subcommodities per commodity is 12; the
minimum is 1; and the maximum is 154.
16 There are 250 such words including “commercial” and “component.” The Internet Appendix provides a full list.
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(the least specific words).17 Overall we consider 7,735 commodity words.
For instance, the last row of Table 1 illustrates that words such as film,
projectors, photoengraving, and microfilm characterize the BEA commodity
“photographic and photocopying equipment.”
The “Detailed Item Output” table also provides metrics of economic
importance. We compute the relative economic contribution of a given
subcommodity (ω) as the dollar value of its production relative to its
commodity’s total production (see the last column of Table 1). Each word in a
subcommodity’s textual description is assigned the same ω. Because a word can
appear in several subcommodities, we sum its ω’s within a commodity. A given
commodity word is important if this fraction is high. We define the commodityword correspondence matrix (CW ) as a three-column matrix containing: a
commodity, a commodity word, and its economic importance (e.g., {#333315,
projector, 0.044} or {#333315, photolithography, 0.162}.
Next, we compute the intensity of vertical relatedness between pairs of
commodities. We construct the sparse square matrix V based on the extent
to which a given commodity is vertically linked (upstream or downstream) to
another commodity. From the Make table, we create SH ARE, an I ×C matrix
(Industry × Commodity) that contains the percentage of commodity c produced
by a given industry i. The U SE matrix is a C ×I matrix that records the dollar
value of industry i’s purchase of commodity c as input. The CF LOW matrix
is then given by U SE ×SH ARE, and is the C ×C matrix of dollar flows from
an upstream commodity c to a downstream commodity d. Similar to Fan and
Goyal (2006), we define the SU P P matrix as CF LOW divided by the total
production of the downstream commodity d. SU P P records the fraction of
commodity c that is used as an input to produce commodity d. Similarly, the
matrix CU ST is given by CF LOW divided by the total production of the
upstream commodities c, and it records the fraction of commodity c’s total
production that is used to produce its commodity d. The V matrix is then
defined as the average of SU P P and CU ST . A larger element in V indicates
a stronger vertical relationship between commodities c and d.18 Note that V is
sparse (i.e., most commodities are not vertically related) and is nonsymmetric
as it features downstream (Vc,d ) and upstream (Vd,c ) directions.
Figure 1 presents a snapshot of the direction and intensity of upstream
and downstream vertical relatedness associated with the “photographic
and photocopying equipment” commodity (#333315). As measured by
V , this commodity is downstream to “semiconductor and related device
manufacturing” (#334413) and “coated and laminated paper, packaging paper,
and plastics film manufacturing” commodities (#32222A), which supply 2.2%
and 1.4% of their respective production to the “photographic and photocopying
17 Hoberg and Phillips (2010) use a similar tercile approach to discard the most broad/vague words.
18 Alternatively, we consider in unreported tests the maximum between SU P P and CU ST , and also SU P P , or
CU ST alone, to define vertical relatedness. Our results are robust.
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Semiconductor and related
device manufacturing
(#334413 – 2.2%)
Coated and laminated
paper, packaging paper and
plascs film manufacturing
(#32222A) – 1.4%
Paperboard container
manufacturing
(#3222210 – 1.1%)
Support acvies for
prinng
(#323120 – 1%)
Electronic and precision
equipment repair and
maintenance
(#33331A – 0.2%)
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Photographic and
photocopying equipment
manufacturing
Vending, commercial,
industrial and office
machinery manufacturing
(#811200 – 0.3%)
Figure 1
Example of BEA commodity-commodity vertical relatedness (the V matrix)
This figure presents a snapshot of the direction and intensity of upstream and downstream vertical relatedness
between commodities based on the 2002 BEA input-output tables (the V matrix), focusing on the “Photographic
and photocopying equipment manufacturing” commodity (#333315). We display some upstream and downstream
commodities and the intensity of their vertical relatedness based on the elements of the matrix V .
equipment” commodity. This commodity is itself upstream to the “support
activities for printing” (#323120) and “electronic and precision equipment
repair and maintenance” (#33331A) commodities, supplying 1% and 0.2%
of its production to these commodities.
2.3 Text-based vertical relatedness
We combine the vocabulary in firm 10-Ks and the vocabulary defining the BEA
IO commodities to identify vertical relatedness between firms. The intuition is
as follows. Because vertical relatedness is observed from the IO tables at the IO
commodity level (see description of the matrix V above), we can score every
pair of firms i and j based on the extent to which they are located upstream or
downstream to each other by (1) mapping i’s and j ’s text to the subset of IO
commodities it provides, and (2) determining the intensity of i and j ’s vertical
relatedness using the vertical relatedness matrix V .
We proceed in three steps. First, we link each firm-year in the
Compustat/Edgar universe to BEA IO commodities by computing the similarity
between the given firm’s business description and the textual description of
each BEA commodity. We limit attention to words that appear in the Hoberg
and Phillips (2016) post-processed universe based on firms’ 10-Ks. Although
uncommon, a firm will sometimes mention its customers or suppliers in its
10-K’s product description. For example, a coal manufacturer might mention
that its products are “sold to” the steel industry. To ensure that vocabulary
from firms’ 10-K product descriptions is not contaminated by such vertical
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U Pi,j = [B ·V ·B ]i,j ,
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links, we remove any mentions of customers and suppliers using eightyone typical phrases listed in the Internet Appendix.19 We then represent
both firm vocabularies and BEA commodity vocabularies as vectors having
a length equal to the number of nouns and proper nouns appearing in 10-K
business descriptions in each year (63,367 in 1997, e.g.), with each element
corresponding to a single word. By representing BEA commodities and firm
vocabularies as vectors in the same space, we are able to assess firm and
commodity similarity.
Our next step is to compute the “firm to IO commodity correspondence
matrix” B. This matrix has dimension M ×C, where C is the number of IO
commodities, and M is the number of firms. An entry Bm,c (row m, column
c) is the cosine similarity of the text in the given IO commodity c, and the
text in firm m’s business description. In this cosine similarity calculation,
commodity word vector weights are assigned based on the words’ economic
importance from the CW matrix (see above), and firm word vectors are equallyweighted following Hoberg and Phillips (2016). We use cosine similarity
because it controls for document length, is bounded in [0,1], and is well
established in computational linguistics (see Sebastiani 2002). The matrix B
thus indicates which IO commodities a given firm’s products are most similar
to. Intuitively, the product description of a firm (say m) that manufactures and
sells photocopying equipment will appear largely similar to the text for the
“photographic and photocopying equipment” commodity (say c), so that the
element Bm,c will be large.
We then measure the extent to which firm i is located upstream relative to
firm j using the following triple product:
(1)
which results in an M ×M matrix of upstream-to-downstream relatedness
between firms i to firms j . Note that direction is important, and the U Pij
matrix is not symmetric. Upstream relatedness of i to j is thus the i’th row
and j ’th column of this matrix. Firm-pairs receiving the highest scores for
vertical relatedness are those having vocabulary that maps most strongly to
IO commodities that are vertically related to matrix V (constructed only using
BEA relatedness data). Thus, firm i is located upstream from firm j when i’s
business description is strongly associated with commodities used to produce
other commodities whose description resembles firm j ’s product description.
Downstream relatedness is simply the mirror image of upstream relatedness,
DOW Ni,j = U Pj,i . We repeat this procedure for every year and create a timevarying network of firm-pairwise vertical relatedness. In section 6, we also use
the diagonal entries of the triple product (U Pi,i ) to measure the extent to which
a given firm is “vertically integrated” based on whether its business description
contains many word pairs that are vertically related.
19 We think this step is important, but our results are robust if we exclude this step.
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film
laminate
plasc
gelan
semiconductor
resin
paper
wax
picture
photocopy
screen
blueprinng
periodical
microfilm
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projector
Coated and laminated
paper, packaging paper and
plascs film manufacturing
(#32222A) – 1.4%
Photographic and
photocopying equipment
manufacturing
camera
library
edion
books
binding
Support acvies for
prinng
(#323120 – 1%)
lithography
Figure 2
Vertical relatedness between words based on BEA
This figure displays how we use vertical relatedness between commodities (on the right) to compute vertical
relatedness between words in the corresponding commodity vocabularies. For instance, the word “photocopy”
(part of the vocabulary of our previous example) is downstream of “plastic,” “semiconductor,” and “resin,”, but
upstream of “periodical,” “book,” and “library.”.
Figures 2 and 3 illustrate the intuition for the triple product B ·V ·B . Figure 2
depicts how we use vertical relatedness between commodities (on the right) to
compute vertical relatedness between words in the corresponding commodity
vocabularies. For instance, the word “photocopy” (part of the vocabulary of our
previous example) is downstream relative to “plastic,” “semiconductor,” and
“resin,” but upstream relative to “periodical,” “book,” and “library.” Figure 3
depicts some words extracted from the 10-K business description of two sample
firms, key to constructing the B matrices in Equation (1). Our measure of
vertical relatedness thus intuitively uses the vertical word mappings from the
BEA data (as in Figure 2), and the words in firm business descriptions to
map specific firm-pairs to the BEA vertically related vocabularies. The arrows
indicate vertical relatedness between words, highlighting that the firms A and
B have nontrivial vertical relatedness (U PA,B is large), as firm A’s business
description contains many words that are upstream relative to many words in
firm B’s business description.
As an illustration, Table 2 presents a snapshot of the U Pij pairwise vertical
network in 2010, focusing on two upstream and downstream companies. The
upper part of the table displays a representative set of companies that are
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Innovation Activities and Integration through Vertical Acquisitions
Firm A
photocopy
Firm B
projector
camera
screen
xxx
blueprinng
xxx
bindings
periodical
lythography
edion
library
xxx
books
xxx
xxx
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Figure 3
Illustration of firm-pair vertical relatedness (U PA,B )
This figure illustrates some words extracted from the 10-K business description of two sample firms, key to
constructing the B matrices in Equation (1). Our measure of vertical relatedness uses the vertical word mappings
from the BEA data (as in Figure 2) and the words in firm business descriptions to map specific firm-pairs to the
BEA vertically related vocabularies. The arrows indicate vertical relatedness between words, highlighting that
the firms A and B have nontrivial vertical relatedness (U PA,B is large).
upstream to Tesla, an electric car manufacturer. These includes companies
selling auto parts (e.g., Sorl Auto Parts), metals (e.g., Titanium Metals),
electronic instruments (e.g., Cabot Microelectronics), or hydraulics products
(e.g., Sun Hydraulics). In the bottom part of the table, we display a set
of companies that are downstream from Encore Wire, a manufacturer of
copper and aluminum wires. Downstream-located companies includes firms
active in the car (e.g., General Motors), electronics (e.g., Pulse Electronics),
communication (e.g., Atheros Communications), energy (e.g., Massey Energy),
or pumps (e.g., Gorman-Rupp) business.
It is important to note our text-based measure of pairwise vertical relatedness
is designed to capture the extent to which two firms operate in vertically related
product markets (e.g., Encore Wire and Caterpillar from Table 2). It does not
measure actual shipments (if any) between pairs of firms. This distinction is
important in light of the recent analysis of Atalay, Hortacsu, and Syverson
(2014) who document limited physical shipments within vertically integrated
firms in the United States, and suggest that intangible assets are important
drivers of vertical boundaries. Our new construct thus provides a broad measure
of vertical relatedness, which captures the “potential” for vertical integration
between firm pairs.
2.4 NAICS-based vertical relatedness
As a benchmark for evaluating our new text-based pairwise vertical network, we
also consider the NAICS-based vertical relatedness network used in previous
research (see, e.g., Fan and Goyal 2006). To compute firms’ pairwise vertical
relatedness based on NAICS, we follow the literature and construct a matrix Z,
which is analogous to the matrix V in the preceding section but is based on BEA
industries instead of commodities. Following common practice in the literature,
we map BEA industries to NAICS industries and use two numerical thresholds
to identify meaningful levels of vertical relatedness between industries: 1%
and 5%. A given industry i is deemed as upstream (downstream) relative to
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Table 2
Vertical relatedness examples: Tesla and Encore Wire
Firm i
(Upstream)
Year
Vertical Score
TESLA INC
TESLA INC
TESLA INC
TESLA INC
TESLA INC
TESLA INC
TESLA INC
TESLA INC
TESLA INC
TESLA INC
TESLA INC
TESLA INC
TESLA INC
TESLA INC
...
ALLIED MOTION TECHNOLOGIES
MASSEY ENERGY CO
BEACON ROOFING SUPPLY INC
GENERAL MOTORS CO
CATERPILLAR INC
TESLA INC
PULSE ELECTRONICS CORP
UNIVERSAL DISPLAY CORP
GORMAN-RUPP CO
HARLEY-DAVIDSON INC
ADVANCED BATTERY TECH INC
ATHEROS COMMUNICATIONS INC
STEEL DYNAMICS INC
...
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
...
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
...
0.02294
0.01273
0.01276
0.02227
0.02293
0.01988
0.02070
0.02005
0.01567
0.01466
0.02239
0.01696
0.02560
0.01275
...
0.01425
0.01302
0.01452
0.01866
0.01692
0.01445
0.01945
0.01260
0.02346
0.01379
0.01695
0.01553
0.02823
...
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MEASUREMENT SPECIALTIES INC
EDAC TECHNOLOGIES CORP
DIODES INC
FUEL SYSTEMS SOLUTIONS INC
SORL AUTO PARTS INC
CLEANTECH SOLUTIONS INTL INC
SUN HYDRAULICS CORP
ENCORE WIRE CORP
CABOT MICROELECTRONICS CORP
LITTELFUSE INC
TITANIUM METALS CORP
COOPER TIRE & RUBBER CO
HONEYWELL INTERNATIONAL INC
GENERAL ELECTRIC CO
...
ENCORE WIRE CORP
ENCORE WIRE CORP
ENCORE WIRE CORP
ENCORE WIRE CORP
ENCORE WIRE CORP
ENCORE WIRE CORP
ENCORE WIRE CORP
ENCORE WIRE CORP
ENCORE WIRE CORP
ENCORE WIRE CORP
ENCORE WIRE CORP
ENCORE WIRE CORP
ENCORE WIRE CORP
...
Firm j
(Downstream)
This table provides examples of vertical relatedness for two illustrative companies in 2010 based on the network
matrix U Pij , defined in Section 2. The table displays the upstream (i ) and downstream (j ) firms, the year, and
the vertical score for each firm-pairs.
industry j when the flow of goods Zij (Zj i ) is larger than this threshold. To
create a network of pairwise vertical relatedness between firms (henceforth
NAICS-based network), we simply assign firms into NAICS industry based on
the NAICS code reported by each firm in Compustat. Therefore, the degree
of vertical relatedness between firms corresponds to that of their respective
industries. We find that the 1% and 5% flow thresholds generate NAICS-based
vertical relatedness networks that have granularity of 1.32% and 9.17%, which
means that 9.17% of randomly chosen firm pairs are vertically related in this
network. For simplicity, we label these vertical networks as “NAICS-1%” and
“NAICS-10%”, respectively.
To ensure our textual networks are comparable, we choose two analogous
textual granularity levels: 10% and 1%. These two text-based vertical networks
define firm pairs as vertically related when they are among the top 10% and top
1% most vertically related firm-pairs using the textual scores. We label these
networks as “Vertical Text-10%” and “Vertical Text-1%.” Note that the NAICSbased networks rely not only on the quality of NAICS industries to delineate
product markets, but also on the mapping between NAICS and BEA industries.
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Table 3
Vertical network statistics and validity tests
Network:
Vert. text-10%
Vert. text-1%
NAICS-10%
NAICS-1%
TNIC-3
10%
1.52%
0.77%
0.60%
0.85%
100%
10%
9.63%
100%
10%
1%
2.50%
1.01%
0.59%
1.07%
100%
100%
1.87%
100%
100%
9.17%
2.87%
0.36%
0.12%
0.36%
10.59%
1.17%
51.20%
20.42%
2.39%
1.32%
3.79%
0.20%
0.11%
0.20%
13.66%
1.17%
36.97%
21.03%
1.85%
2.36%
100%
36.61%
36.27%
39.55%
7.17%
1.18%
55.81%
11.63%
2.68%
21.49%
6.36%
14.47%
5.09%
14.09%
4.05%
0.76%
3.01%
0.35%
12.293
A: Networks’ statistics
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Granularity
% of pairs in TNIC-3
% of pairs in the same SIC
% of pairs in the same NAICS
% of pairs in the same SIC or NAICS
% of pairs in Vert. Text-10%
% of pairs in Vert. Text-1%
% of pairs that include a financial firm
% of (no fin.) pairs in Vert. Text-10%
% of (no fin.) pairs in Vert. Text-1%
B: Actual supplier-customer relationships
Supp-cust pairs (Compustat)
(38,734 pairs)
Supp-cust pairs (CIQ)
(12,293 pairs)
This table presents the characteristics of our five distinct networks: Text-10% and Text-1% networks correspond
to vertical networks based on textual analysis set at a 10% and, respectively, 1% granularity level, NAICS-1% and
NAICS-5% correspond to vertical networks based in the 2002 BEA Input-Output Table with relatedness cutoffs
of 1% and 5%, respectively, and TNIC corresponds to the horizontal text-based Network Industry Classification
developed by Hoberg and Phillips (2010). Panel A presents the fraction of firm-pairs in each network that
are also present in other networks. Panel B presents the fraction of firm-pairs in each network that are also
present in the networks of actual customer-supplier linkages, based on Compustat segments and Capital IQ client
announcements.
Both industry classifications suffer from the same time-fixed and all-or-nothing
limitations of SIC codes noted in Hoberg and Phillips (2016). A key advantage
of the textual vertical network is that it relies on neither, and instead relies on
the more detailed BEA commodity space. Unlike existing measures, our textbased measure of vertical relatedness generates a set of vertically related peers
that is customized to each firm’s unique product offerings, and varies over time
as firms dynamically update their product descriptions.
2.5 Vertical network statistics and validation
Panel A of Table 3 presents comparative statistics for four vertical and one
horizontal relatedness networks: Vertical Text-10%, Vertical Text-1%, NAICS10%, NAICS-1%, and the TNIC-3 network developed by Hoberg and Phillips
(2016). The first four capture pairwise vertical relatedness, and the TNIC-3
network captures pairwise horizontal relatedness. The first row shows that the
NAICS-10% and NAICS-1% networks have granularity levels of 9.17% and
1.32%, respectively. These levels, by design, are comparable to the 10% and
1% levels for the “Vertical Text-10%” and “Vertical Text-1%” networks.
Reassuringly, the second to fourth rows show that the four vertical networks
overlap little with the horizontal TNIC-3 network, and horizontal networks
based on firms’ classified in the same SIC or NAICS industries. Thus, horizontal
contamination is low. However, the fifth and sixth rows illustrate that the vertical
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networks are also quite different. Only 10.59% of firm-pairs in the NAICS10% network overlap with the Vertical Text-10% network, and only 1.17% of
firm-pairs overlap for the Vertical Text-1% and NAICS-1% networks.
A key reason for this difference relates to financial firms. The eighth row
shows that financial firm pairs are rarely classified as vertical by the textbased vertical networks (9.63% and 1.87% of linked pairs for the 10% and
1% networks). In contrast, financial firms account for a surprisingly large
51.20% and 39.97% of firm-pairs in the NAICS-based vertical networks.
When we discard financials, the last two rows show that overlap between
our text-based and NAICS-based networks roughly doubles. Because theories
of vertical integration are based on nonfinancial issues, such as relationshipspecific investment and ownership rights, these results further validate the use
of our text-based network.
We perform several analyses to assess the external validity of our vertical
relatedness network. First, we examine the extent to which our measure predicts
actual vertical relationships between firm pairs. Similar to Barrot and Sauvagnat
(2016) we rely on Compustat segment files to identify customer representing
more than 10% of firms’ sales. For our sample period, we obtain 38,734
supplier-customer pairs (excluding financials and utilities), corresponding to
4,689 unique suppliers and 2,306 customers.20 Alternatively, we use new
client announcements from Capital IQ’s “Key Developments” database and
obtain 12,293 announced supplier-customer pairs (4,474 suppliers and 4,459
customers) where both partners have available links to Compustat in our sample
period.21 Panel B of Table 3 indicates that our Vertical Text-10% network
captures more than 21% of the Compustat customer-supplier pairs, whereas
only 14% are classified as vertically related using the NAICS-10% network. The
text-based network is thus 50% more effective. A similar picture emerges when
we focus on the Capital IQ new client announcements, where our text-based
measure performs 34% better than the NAICS-based measure.
Second, we study how firms that are related vertically respond to trade
credit shocks. Intuitively, firms operating in vertically related markets should
experience negatively correlated shocks in accounts payables versus accounts
receivables due to their supply chain adjacency. For example, a shock to an
upstream market’s receivables should be associated with a similar shock to
the downstream market’s payables. In contrast, firms operating in horizontally
related markets should experience trade-credit shocks that are positively
correlated. For brevity, we present the details of the test and results verifying
20 Regulation SFAS No. 131 requires that firms disclose information about larger customers. We obtain customer-
suppliers links using the replication kit of Barrot and Sauvagnat (2016) posted on the Quarterly Journal of
Economics Web site.
21 Capital IQ records corporate announcements from over 20,000 sources including public news announcements,
company press releases, regulatory filings, call transcripts, and company Web sites and filters these data to
eliminate duplicates, identify the companies involved, and categorize each announcement into event categories
(we focus on new clients). These data are available starting from 2002.
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that these properties hold significantly for our text-based measures of vertical
relatedness in the Internet Appendix. In contrast, these properties do not hold
for the NAICS-based measure.
3. Innovation and Vertical Acquisitions
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To test our main hypothesis, we study the determinants of vertical acquisitions.
We focus on targets (i.e., the sellers of assets) as they are the party that loses
control rights due to the transaction. Our baseline test focuses on the distinction
between unrealized and realized innovation, and how both affect the likelihood
of becoming a target in a vertical acquisition.
3.1 Sample and definition
We gather data on mergers and acquisitions from the Securities Data
Corporation SDC Platinum database. We consider all announced and completed
U.S. transactions with announcement dates between January 1, 1996 and
December 31, 2013, that are coded as a merger, an acquisition of majority
interest, or an acquisition of assets. As we are interested in situations where
the ownership of assets changes hands, we only consider acquisitions that give
acquirers majority stakes. Following the convention in the literature, we limit
attention to publicly traded acquirers and targets, and we exclude transactions
that involve financial firms and utilities (SIC codes between 6000 and 6999
and between 4000 and 4999). To distinguish between vertical and nonvertical
transactions, we require that the acquirer and the target have available data in
the merged Compustat/Edgar universe (see Section 2.2.1).
Panel A of Table 4 indicates that the sample consists of 4,974 transactions
and tabulates how many of these transactions are classified as vertical using
various networks. We find that 39% are vertically related using the Vertical
Text-10% network, whereas just 13% are vertical according to the NAICS-10%
network. Because both networks have similar granularity levels, it is perhaps
surprising that the networks disagree sharply on the raw intensity of vertical
acquisitions. Also, given the granularity of 10%, if transactions are random,
we would expect to classify 10% of transactions as vertical. The fact that we
find 39% is evidence that vertically acquisitions are relatively common. We
also find that with both networks, vertical deals are almost evenly split between
upstream and downstream transactions.
To further highlight differences between vertical networks, we examine
whether transactions classified as vertical lead to actual increases in acquirers’
vertical integration. We do so by searching for the terms “vertical integration”
and “vertically integrated” in each firm’s 10-K (excluding cases where the
firm indicates that it is not integrated or lacks integration), and define a binary
variable identifying when a firm indicates that it is vertically integrated (V I10k ).
We then regress it on binary variables indicating whether a firm was a vertical
or nonvertical acquirer in the last 3 years (as well as firm and year fixed effects).
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Table 4
Mergers and acquisitions: Sample description
Measure:
All
Deal type:
(N )
Vertical
Text-based
Nonvert.
Vertical
NAICS-based
Nonvert.
4,974
1,919
38.58%
3,055
61.42%
626
12.59%
4,348
87.41%
A: Sample description
# Transactions
% Vertical (Nonvert.)
952
967
229
320
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# Upstream
# Downstream
B: Mentions of vertical integration after transactions
0.037∗∗
(0.018)
(dep.var :) V I10k
(firm and year FE)
(dep.var :) V I10k
(firm and year FE)
-0.014
(0.011)
0.033
(0.026)
0.006
(0.011)
0.41%
0.40%
580
0.55%
0.91%
4,049
C: Combined acquirers and targets returns
CAR(0)
CAR(-1,1)
# Transactions
0.54%
0.86%
4,629
0.61%
0.94%
1,807
0.49%
0.79%
2,822
Vert. score
Year
Value ($ mio)
Type
0.0059
0.0042
0.0241
0.0053
0.0290
0.0084
2007
2003
2004
2005
1999
2006
985
1,005
1,395
1,612
4,408
10,291
Upstream
Downstream
Upstream
Upstream
Upstream
Downstream
D: Examples of vertical deals
Acquirer
Target
Western Digital
Conexant
General Electic
3M Co
United Technologies
Thermo Electron
Komag
GlobeSpanVirata
Edwards Systems
Cuno
Sundstrand
Fischer Scientific
This table presents the characteristics of the transaction sample. Panel A displays statistics for vertical and
nonvertical transactions (nonfinancial firms only). A transaction is classified as vertical if the acquirer and target
are pairs in the Vertical Text-10% network or the NAICS-10% network. Panel B displays regression coefficients
(and standard errors) from specifications regressing a binary variable indicating mentions of vertical integration
in firms’ 10-K (V I10k ) on binary variables indicating whether firms were vertical or nonvertical acquirers in
the last 3 years, based on our the text-based or NAICS-based vertical networks. Specifications include firm and
year fixed effects. Panel C displays the average cumulated abnormal announcement returns (CARs) of combined
acquirers and targets, obtained from a market model estimated on 250 days prior to events. Panel D displays
examples of large vertical deals (both upstream and downstream acquisitions), together with the vertical score
between parties measures on the year of transaction. ∗ p > .1; ∗∗ p > .05; ∗∗∗ p > .01.
Panel B of Table 4 indicates that vertical acquisitions identified using our textbased measure are followed by a significant increases in mentions of vertical
integration (with a t-statistic of 2.04), while we detect no significant changes
in the language associated with integration after vertical transactions identified
using the NAICS-based measure (t-statistic of 1.27). These results suggest
that the accumulated noise associated with NAICS-based vertical measure is
substantial.
Panel C also displays the average abnormal announcement return (expressed
as a percentage) of combined acquirers and targets in vertical and nonvertical
transactions. We present these results mainly to compare with previous research
(based on SIC or NAICS codes). Confirming existing evidence, combined
returns across all transactions are positive and range from 0.54% to 0.86%.
We also find that when vertical transactions are identified using our text-based
measure, the combined returns are larger for vertical relative to nonvertical
20
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Innovation Activities and Integration through Vertical Acquisitions
Table 5
Summary statistics
Mean
SD
Min
Max
#Obs.
R&D/sales
Patents/assets
log(assets)
log(age)
PPE/assets
Segments
MB
CF/assets
OI/sales
Sales growth
HHI
End users
Target
Vertical target
Nonvertical target
corr(R&D/sales,Patents/assets)
0.108
0.009
5.680
2.896
0.259
1.477
1.982
0.023
0.047
0.169
0.237
0.470
0.065
0.024
0.041
0.321
0.277
0.028
1.913
1.108
0.225
0.936
1.491
0.204
0.359
0.412
0.198
0.077
0.246
0.153
0.198
—
0.000
0.000
1.780
0.000
0.006
1.000
0.561
−1.048
−2.236
−0.545
0.016
0.034
0.000
0.000
0.000
—
2.373
0.186
10.338
4.984
0.898
12.000
9.521
0.366
0.628
2.324
1.000
0.985
1.000
1.000
1.000
—
61,463
61,463
61,463
61,463
61,463
61,463
61,463
61,463
61,463
61,463
61,463
61,463
61,463
61,463
61,463
61,436
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Variable:
This table displays summary statistics for the main variables used in the analysis. Appendix defines the variables.
transactions. This supports the idea that vertical deals are value creating as in
Fan and Goyal (2006).
Finally, panel D presents six examples of large vertical acquisitions in
our sample, representing both upstream and downstream purchases. Western
Digital, a disk drive manufacturer, is an example of a downstream firm buying
an upstream firm. They purchased Komag Inc. in 2007, a producer of thin-film
discs used in hard drives. Another example of upstream integration is GE’s
acquisition of Edwards Systems Technology in 2004, which gave GE control
of Edward’s fire detection technology, used in GE’s broad security offering.22
In contrast, Conexant, a semiconductor manufacturer, integrated downstream
through the acquisition in 2003 of Globespan Virata Inc., a maker of broadband
communications products that use semiconductor chips.
3.2 Measuring unrealized and realized innovation
To measure unrealized and realized innovation and map them to transactions,
we merge the SDC transaction data with our merged Compustat/SEC EDGAR
firm-year database. We discard financial firms and utilities, and firm-year
observations with sales lower than $5 million.23 The resultant sample includes
61,463 firm-year observations over the period 1996-2013, corresponding to
8,283 distinct firms.
We empirically characterize firms’ R&D intensity as unrealized innovation
and patenting intensity as realized innovation. A firm’s R&D intensity is the
22 Will Woodburn, CEO of GE Infrastructure, indicated that Edwards’ technology will enable GE to offer an
“integrated facility management solution to partners and end users.” See Buildings (2004).
23 The Internet Appendix shows that such a size threshold does not affect our conclusions.
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dollar amount spent on R&D in a given year divided by sales.24 A firm’s
patenting intensity is the number of patents granted in a given year divided
by assets.25 We obtain information on patent grants from the United States
Patent and Trademark Office (USPTO) for each firm in our sample between
1996 and 2010 by combining information from the NBER patent data set and
the recent data compiled by Kogan et al. (2017). We extend these data by
manually matching all patent grants to firms in our sample between 2011 and
2013 using assignee names. We winsorize both variables (and all continuous
variables used in the analysis) at the 1% and the 99% level, and the appendix
defines the variables.
Table 5 presents summary statistics. The average R&D intensity is 0.108 with
a standard deviation of 0.277. The average patenting intensity is 0.009 (i.e., one
patent per year for each $110 million of assets) with a standard deviation of
0.028. We also note that the correlation between firms’ R&D and patenting
intensity is 0.321. Finally, the odds of being purchased by a vertically related
buyer in a given year is 2.4%, whereas the odds of a nonvertical acquisition is
4.1%. The other variables (e.g., size, age, or cash flow) are in line with existing
research.
3.3 Main results
To explore the link between the stage of development of firms’ innovation
and vertical acquisitions, we begin by reporting summary statistics in Table 6
and find a large difference in the innovation characteristics of firms purchased
in vertical and nonvertical deals. When compared to firms that participate in
no acquisitions over our sample period (nonmerging firms), targets in vertical
transactions spend significantly less on R&D and are granted more patents. In
contrast, targets in nonvertical acquisitions spend more on R&D and are granted
fewer patents. In panel B, each target (vertical and nonvertical) is directly
compared to a matched firm with similar characteristics that did not transact in
the past 3 years.26 We find similar results.
We confirm these univariate results by estimating probit models in which
the dependent variable is a binary variable indicating whether a given firm is a
target in a vertical transaction in a given year. The main independent variables
are the contemporaneous firms’ R&D and patenting intensity. We include year
fixed effects and cluster standard errors at the year and industry level based on
24 Following Koh and Reeb (2015), we replace missing firm-year R&D intensity by the median value of the industry
(based on TNIC-3 industries). We show in the Internet Appendix that our results are similar if we restrict our
sample to firms that report nonmissing R&D or if we directly control for the intensity of missing firms’ R&D as
in Seru (2014).
25 We focus on patents awarded by “grant year” as opposed to “application year” because our hypothesis concentrates
on changes in investment incentives and holdup risk that materialize when realized innovation is legally protected.
The use of grant dates also eliminates potential truncation bias (Lerner and Seru 2017).
26 For every transaction, matched targets are the nearest neighbors from a propensity score estimation based on
Fixed Industry Classification (FIC) industries (Hoberg and Phillips 2016) and firm size.
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Innovation Activities and Integration through Vertical Acquisitions
Table 6
Vertical acquisitions: Deal-level analysis
Variable:
R&D/sales
Patents/assets
0.053
0.136
0.109
(−11.73)∗∗∗
(−8.82)∗∗∗
(4.21)∗∗∗
0.010
0.009
0.008
(0.69)
(3.25)∗∗∗
(2.70)∗∗∗
0.053
0.093
(−6.61)∗∗∗
0.010
0.006
(4.66)∗∗∗
0.136
0.115
(2.76)∗∗∗
0.009
0.008
(2.66)∗∗∗
A: Whole sample
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(i) Vert. targets
(ii) Nonvert. targets
(iii) Nonmerging firms
t -statistic [(i)-(ii)]
t -statistic [(i)-(iii)]
t -statistic [(ii)-(iii)]
B: Matched targets
(i) Vert. targets
(ii) Matched vert. targets
t -statistic [(i)-(ii)]
(i) Nonvert. targets
(ii) Matched nonvert. targets
t -statistic [(i)-(ii)]
This table presents univariate comparisons of the R&D and patenting intensity of firms’ acquired in vertical and
nonvertical transactions, as well as firms that never participate in any transactions. Transactions are defined as
vertical when the acquirer and target are in pairs in the Vertical Text-10% network. In panel A, we compare targets
of vertical and nonvertical deals, and nonmerging firms. In panel B, each target is compared to a "matched"
nonmerging target using a propensity score model based on industry, size, and year. The appendix contains
the definitions of all the variables. We report t -statistics corresponding to tests of mean differences. ∗ p > .1;
∗∗ p > .05; ∗∗∗ p > .01.
the FIC classification from Hoberg and Phillips (2016). We also standardize
each independent variable by their sample standard deviation for convenience.
Table 7 confirms that unrealized and realized innovation have opposite effects
on firms’ vertical boundaries. In the first column, the estimated coefficient on
firms’ R&D intensity is significantly negative, supporting our hypothesis that
R&D-intensive firms are more likely to stay separate and retain residual rights
of control. Separation is optimal when innovation is unrealized as it preserves ex
ante incentives to maintain investments in innovation. In contrast, the coefficient
on firms’ patent intensity is positive and significant, supporting our prediction
that realized innovation protected by patents fosters the benefits of integration,
and increases the likelihood of vertical acquisitions as it mitigates risk of ex
post holdup.27
Following previous research on acquisition incidence (e.g., Hoberg and
Phillips 2010), Column 2 adds several control variables to capture other
potential determinants of vertical acquisitions. These include firm size, age,
asset structure (the ratio of fixed assets and the number of business segments),
profitability (cash flow over assets and operating income over sales) and growth
prospects (market-to-book ratio and sales growth). We also include a control for
product market concentration (HHI), and a text-based variable identifying the
27 We report a similar estimation that identifies vertical acquisitions using the NAICS-based network (NAICS-10%)
in the Internet Appendix. We continue to find our main results regarding R&D and patenting intensity on vertical
acquisitions, albeit with materially weaker economic and statistical magnitudes.
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Table 7
Determinants of vertical acquisitions
Dependent variable:
Specification:
R&D/sale
Patents/assets
log(age)
PPE/assets
Segments
MB
CF/assets
OI/sales
Sales growth
HHI
End users
Year FE
Ind×Year FE
#Obs.
Pseudo. R 2
Main
(2)
−0.247∗∗∗ −0.092∗∗∗
(0.05)
(0.03)
0.063∗∗∗
0.112∗∗∗
(0.01)
(0.01)
0.329∗∗∗
(0.02)
0.079∗∗∗
(0.01)
−0.015
(0.01)
0.081∗∗∗
(0.01)
−0.093∗∗∗
(0.02)
−0.022
(0.02)
−0.027
(0.02)
−0.090∗∗∗
(0.02)
−0.026∗
(0.01)
−0.175∗∗∗
(0.01)
Ind×Yr
(3)
lags
(4)
OLS
(5)
Text
(6)
Ind.
(7)
−0.038∗
(0.03)
0.074∗∗∗
(0.01)
0.246∗∗∗
(0.02)
0.085∗∗∗
(0.01)
−0.021
(0.02)
0.086∗∗∗
(0.01)
−0.032∗
(0.02)
−0.035∗
(0.02)
−0.012
(0.02)
−0.063∗∗∗
(0.02)
−0.068∗∗∗
(0.02)
−0.066∗∗∗
(0.02)
−0.095∗∗∗
(0.03)
0.101∗∗∗
(0.01)
0.329∗∗∗
(0.02)
0.065∗∗∗
(0.01)
−0.003
(0.01)
0.099∗∗∗
(0.01)
−0.075∗∗∗
(0.02)
−0.015
(0.02)
−0.043∗
(0.03)
−0.046∗∗∗
(0.02)
−0.018
(0.02)
−0.171∗∗∗
(0.01)
−0.002∗∗∗
(0.00)
0.005∗∗∗
(0.00)
0.018∗∗∗
(0.00)
0.005∗∗∗
(0.00)
−0.000
(0.00)
0.011∗∗∗
(0.00)
−0.001∗∗∗
(0.00)
−0.002∗∗∗
(0.00)
−0.002∗
(0.00)
−0.003∗∗∗
(0.00)
−0.001
(0.00)
−0.009∗∗∗
(0.00)
−0.038∗
(0.02)
0.015∗
(0.01)
0.314∗∗∗
(0.02)
0.087∗∗∗
(0.01)
−0.020
(0.02)
0.078∗∗∗
(0.01)
−0.072∗∗∗
(0.02)
−0.045∗∗
(0.02)
0.005
(0.02)
−0.102∗∗∗
(0.02)
−0.025∗
(0.02)
−0.179∗∗∗
(0.01)
−0.253∗∗∗
(0.03)
0.169∗
(0.01)
0.331∗∗∗
(0.02)
0.075∗∗∗
(0.01)
−0.016
(0.01)
0.080∗∗∗
(0.01)
−0.060∗∗∗
(0.02)
−0.047∗∗
(0.02)
−0.026
(0.02)
−0.087∗∗∗
(0.02)
−0.040∗∗∗
(0.01)
−0.165∗∗∗
(0.01)
Yes
No
Yes
No
Yes
Yes
Yes
No
Yes
No
Yes
No
Yes
No
61,463
.015
61,463
.129
58,257
.150
52,257
.127
61,463
.033
55,833
.123
61,463
.133
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log(assets)
Prob(Vertical target)
Main
(1)
This table presents results from probit models in which the dependent variable is a dummy indicating whether the
given firm is a target in a vertical transaction in a given year. Vertical transactions are identified using the Vertical
Text-10% network. The first two columns are the baseline models without and with control variables. Column
3 includes industry × year fixed effect, where industries are defined using FIC-100 industries from Hoberg and
Phillips (2016). Column 4 considers (1-year) lagged independent variables. Column 5 reports estimates from
a linear probability model (OLS) instead of probit. Column 6 considers firms R&D and patenting intensities
directly from 10-K mentions. In column 7, all independent variable are computed as industry (equally weighted)
averages, based on TNIC-3 industries. Appendix defines the variables. The independent variables are standardized
for convenience. All estimations include year fixed effects. Standard errors are clustered by FIC-300 industry
and year and are reported in parentheses. ∗ p > .1; ∗∗ p > .05; ∗∗∗ p > .01.
degree to which firms’ products exit the supply chain.28 We continue to observe
opposite effects of firms’ R&D and patenting intensity on vertical acquisitions.
The remaining columns in Table 7 indicate that our results for R&D and
patenting intensity are robust. In Column 3, we add industry×year fixed effects
(based on FIC-100 industries from Hoberg and Phillips 2016) to control for
time-varying industry characteristics. In Column 4, we use lagged values
for all independent variables. In Column 5, we estimate a linear probability
28 We characterize whether a firm’s products or services exit the supply chain using the BEA exit correspondence
matrix, which includes industries present in the Use table, but not in the Make table (retail customers, the
government, and exports). A higher value for End U ser indicates that a firm has a higher fraction of its products
and services sold to retail consumers, the government, or foreign entities.
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model instead of the baseline probit. In Column 6 we measure firms’ R&D
and patenting intensity directly from firms’ 10Ks, which avoids the wellknown measurement problems of Compustat R&D reporting, and incomplete
patent counts when assigning patents to firms (e.g., Lerner and Seru 2017).
Specifically, we count the number of paragraphs mentioning R&D or patents
in each 10K and scale by the total number of paragraphs. Finally, we follow
Acemoglu et al. (2010) and consider industry averages (based on TNIC-3
industries) instead of firm-level independent variables. The main results hold
in all cases.
Our main specification includes both firms’ R&D and patenting intensities
as independent variables, as this allows us to derive separate inferences for
each, while holding the other constant. However, because these variables
are positively correlated (0.321 in our full sample), we also examine if
multicollinearity is a concern. We first examine the variance inflation factors
based on our complete linear model in Column 5, and find that they are small
(2.33 and 1.17) for both firms’ R&D and patenting intensities. This suggests
that multicollinearity is unlikely a problem in our setting. Second, we report in
the Internet Appendix that our results continue to hold if we include firms’ R&D
and patenting intensity separately, if we consider specific subsamples in which
the correlation between R&D and patenting is small, if we focus separately on
upstream or downstream acquisitions, if we eliminate from the sample firms
that are targeted in nonvertical acquisitions (thus comparing vertical acquirers
to nonacquirers), if we focus on targeted firms (thus comparing vertical deals
to nonvertical deals), and in a bootstrap analysis in which we reestimate our
baseline specification 1,000 times on subsamples comprising 3,000 randomly
selected firms.
3.4 Economic magnitudes
Because our main specification is a vertical acquisition probit model,
we measure economic magnitudes using marginal effects (dydx), which
capture the implied change in transaction incidence (dy) in response to
a 1-standard-deviation change in each independent variable (dx). We also
present the predicted transaction incidence for standard percentile breakpoints.
Specifically, we first compute the predicted transaction incidence when all
variables are set to their mean values. We then shift a given variable to its 25th
percentile or 75th percentile (or to its 10th or 90th percentile) and recompute
the predicted probability holding all other variables fixed. We thus report
how predicted transaction probabilities shift as a variable moves across these
breakpoints.
Table 8 confirms that our findings are economically meaningful. The
estimated marginal effect of a 1-standard-deviation increase in firms’ R&D
intensity is associated with a 0.46% percentage point reduction in the
probability of being acquired in a vertical transaction. This shift is economically
large given that it represents one-third of the mean predicted probability of
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Table 8
Implied economic magnitude
25th
(2)
Mean
(3)
75th
(4)
10th
(5)
Mean
(6)
90th
(7)
R&D/sales
Patents/assets
log(assets)
log(age)
PPE/assets
Segments
MB
CF/assets
OI/sales
Sales growth
HHI
End user
−0.46%
0.56%
1.65%
0.39%
−0.07%
0.41%
−0.47%
−0.11%
−0.13%
−0.45%
−0.13%
−0.88%
1.49%
1.24%
0.71%
1.20%
1.41%
1.23%
1.57%
1.37%
1.37%
1.52%
1.43%
1.81%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.33%
1.51%
2.36%
1.56%
1.34%
1.53%
1.31%
1.33%
1.33%
1.31%
1.33%
1.03%
1.50%
1.24%
0.43%
1.03%
1.42%
1.23%
1.62%
1.44%
1.42%
1.65%
1.45%
2.32%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.36%
1.23%
1.60%
3.93%
1.78%
1.29%
1.90%
1.05%
1.31%
1.31%
1.10%
1.24%
0.78%
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dydx
(1)
Measure:
This table displays economic magnitudes associated with vertical acquisition incidence. All magnitudes are
conditional and thus account for the effects of year and all control variables based on our baseline probit model
(reported in Column 2 of Table 7). Column 1 reports marginal effects (dydx ), which capture the implied change
in transaction incidence (dy ) in response to a 1-standard-deviation change in each independent variable (dx ).
The other columns present the implied variation around the unconditional transaction incidence. We compute
the predicted value when all of the independent variables are set to their mean values. We then set each variable
to its 25th percentile or 75th percentile in Columns 2 to 4 or to its 10th or 90th percentile in Columns 5 to 7 and
recompute the predicted probability holding all other variables fixed. We then report how the predicted probability
of being acquired vertically changes when a independent variable moves from its 25th (10th) percentile to its
mean, and then to its 75th (90th) percentile. Appendix defines the variables.
1.36%. Because firms’ R&D intensity has a high degree of skewness, changing it
from the 25th to the 75th percentile lowers acquisition incidence more modestly
from 1.49% to 1.33%. The economic impact of firms’ patenting intensity has
a similarly large 0.56% marginal effect, and acquisition incidence increases
from 1.24% to 1.51% when patenting intensity moves from the 25th to the 75th
percentile.
We also note that the economic magnitude of firms’ R&D and patenting
intensities is large relative to other determinants of vertical acquisitions. For
instance, the marginal effects of both are similar to those of the market-to-book
ratio, sales growth, and age. They are also roughly one-third as large as the
marginal effect of firm size, which is the most important driver of acquisitions.
We conclude that the association between the stage of innovation and vertical
acquisitions is large both nominally and relative to other variables.
4. Incentive Provision and Holdup Costs
To provide further support for our interpretation, we consider specific
predictions regarding the economic mechanisms underlying our hypothesis.
The theory of the firm emphasizes that, because of contract incompleteness,
the integration of vertically related firms comes with both costs and benefits,
so that vertical acquisitions occur as a trade-off between ex ante innovation
incentives and ex post holdup risk. We thus test whether our results are indeed
stronger in subsamples in which the importance of maintaining incentives and
the risk of holdup are particularly elevated.
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4.1 Internal provision of incentives
Grossman and Hart (1986) and Hart and Moore (1990) illustrate that the main
cost of vertical integration operates through a reduction of incentives for the
party that loses residual rights of control. We propose that integration through
vertical acquisitions should be relatively less costly – and therefore more
likely – when the reduction of innovation incentives is less important to the
overall relationship. We propose that this is more likely when the acquirer,
as the new owner of the residual control rights, has the ability to maintain
investment incentives after the acquisition through compensation contracts
and/or by keeping key employees.29 We thus predict that firms’ unrealized
innovation should be a less important driver of their vertical acquisition when
acquirers can better maintain innovation incentives post-acquisition.
To test this prediction we examine how the sensitivity of vertical acquisitions
to firms R&D intensity varies with proxies for the difficulty to maintain
innovation incentives within firms. Our first proxy is based on inventor
mobility. Following Melero, Palomeras, and Wehrheim (2017), we track the
careers of inventors listed on patents (from the PatentView initiative) using
the disambiguation algorithm in Li et al. (2014). We focus on inventors who
receive at least one patent grant with a public firm between 1990 and 2013. We
then compute the fraction of inventors listed on patents in a given year who
move to another firm in the next 5 years. We posit that higher inventor mobility
reveals greater risk of key employee leaving the firm and hence more difficulty
in maintaining innovation incentives ex post.30
We develop two additional measures of the difficulty to maintain innovation
incentives using the text in firm 10-Ks. First, we compute the fraction of a firm’s
10-K paragraphs that contain at least one word from each of the following two
lists: {employee*} and {contributions, retention, incentive, award*, equity,
option, grant, deferred, vest*, stock, compens*, plan*, benefit*, shares}.31
Second, we identify key man insurance as the fraction of 10-K paragraphs
containing the bigram “key man” (excluding “do not”). We propose that
maintaining innovation incentives is relatively easier both for firms mentioning
employee incentives and for firms less worried about losing key employees.
Table 9 reports how these proxies moderate the effect firms’ R&D intensity on
the probability that they are acquired vertically. We do so by adding interaction
terms between each independent variable in our baseline (including year fixed
effects) and an indicator variable identifying observations that have above
median values of each moderating variable (which we label “H I GH ”) defined
29 We thank Oliver Hart for suggesting this interpretation of the theory of the firm.
30 For example, Ouimet and Zarutskie (2016) show that acquirers can successfully retain some high value employees
by offering them compensation increases.
31 ‘*’ indicates a wild card.
27
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Table 9
Contract incompleteness and holdup costs
Dep. variable:
Prob(Vertical target)
Proxies for:
HIGH based on:
Ex ante incentives
Ex post holdup
Employee
incentives
(2)
Keyman
insurance
(3)
Patent
liquidity
(4)
Patent
infringement
(5)
Number of
peers
(6)
−0.065∗∗
(0.03)
−0.087∗∗
(0.05)
0.104∗∗∗
(0.02)
−0.039
(0.03)
−0.184∗∗∗
(0.07)
0.177∗∗∗
(0.07)
0.098∗∗∗
(0.02)
0.017
(0.03)
−0.097∗∗∗
(0.03)
0.097∗∗
(0.05)
0.109∗∗∗
(0.01)
0.058
(0.06)
−0.139∗∗
(0.08)
0.041
(0.09)
0.154∗∗∗
(0.02)
−0.070∗∗∗
(0.03)
−0.124∗∗
(0.06)
0.031
(0.07)
0.160∗∗∗
(0.02)
−0.062∗∗
(0.03)
−0.073∗∗∗
(0.03)
−0.017
(0.06)
0.120∗∗∗
(0.02)
−0.126∗∗
(0.06)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
#Obs.
Pseudo. R 2
61,463
0.133
61,463
0.131
61,463
0.129
61,463
0.131
61,463
0.129
61,463
0.129
p -val. R&D/sales
p -val. Patent/assets
0.024∗∗
0.799
0.020∗∗
0.169
0.021∗∗
0.858
0.963
0.134
0.679
0.042∗∗
0.421
0.027∗∗
R&D/sales
R&D/sales × HIGH
Patents/assets
Patents/assets × HIGH
Controls
Controls × HIGH
Year FE
Year × HIGH FE
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Inventor
mobility
(1)
This table presents results from probit models in which the dependent variable is a dummy indicating whether
the given firm is a target in a vertical transaction in a given year. Vertical transactions are identified using the
Vertical Text-10% network. The independent variables are similar to the baseline specification (Column 2 of
Table 7), augmented with interaction terms between each independent variable (including the year fixed effects)
and indicator variables (“HIGH”) identifying the upper half of the distribution of six different splitting variables.
We define indicator variables and assign firm-year observation into groups every year. The splitting variables
are inventor mobility (Column 1), employee incentives (Column 2), the presence of keyman insurance (Column
3), patent liquidity (Column 4), the intensity of patent infringement (Column 5), and the number of horizontal
peers (Column 6). For brevity, we only report the coefficients on firms’ R&D and patenting intensity and their
respective interactions. Appendix defines the variables. The independent variables and their interactions with
HIGH are standardized for convenience. All estimations include year fixed effects and their interaction with
HIGH. Standard errors are clustered by FIC-300 industry and year and are reported in parentheses. We also
report (bottom of each column) the p-values corresponding to significance tests of the marginal effect of the
interaction terms (see Ai and Norton (2003)). ∗ p > .1; ∗∗ p > .05; ∗∗∗ p > .01.
separately in each year.32 For brevity, we only report our key R&D and patenting
variables and the interaction terms. We also report (bottom of each column)
the p-values corresponding to significance tests of the marginal effect of the
interaction terms (see Ai and Norton 2003).
In the first column, we find a negative and significant interaction between
R&D/sales and H I GH , which indicates that the negative association between
firms’ R&D intensity and the odds of vertical acquisitions is significantly
stronger for firms with higher inventor mobility. Separation is more likely for
these firms when their innovation is still unrealized. Analogously, Columns 2
and 3 show that vertical acquisitions are less sensitive to firms R&D intensity
when firms rank higher on their ability to provide incentives through contracts.
Remarkably, and especially consistent with these tests pertaining to ex ante
32 To simplify interpretation, we scale each interaction term by its sample standard deviation.
28
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Innovation Activities and Integration through Vertical Acquisitions
incentives only, the interaction terms are only significant for firms’ R&D
intensity.
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4.2 Holdup risk
As originally noted by Williamson (1979), the main benefit of vertical
integration operates through a reduction in the risk of opportunistic behavior
by the party holding residual rights of control. Vertical acquisitions and the
resultant joint ownership of the realized innovation should thus be more likely
when the risk of holdup is higher. On this ground, the likelihood that a firm is
acquired vertically should be more sensitive to its realized innovation when the
risk of holdup is higher for the other party.
We consider three variables indicating elevated holdup risk. First, we propose
that patents with a more liquid secondary market (sales and licensing) are
less prone to holdup risk, as the threat of selling the technology can limit
opportunistic behaviors. We follow Serrano (2010) and Bowen (2016) and use
the USPTO Patent Assignment Database (PAD) to compute the annual fraction
of public firms’ existing patents that are sold or licensed to other public firms
between 1996 and 2013, and we average this rate over each firm’s industry.33
Second, we focus on the frequency with which patents are infringed in a given
market (e.g., Tirole 2016), arguing that holdup risk is lower when intellectual
property rights are less stringent and more often infringed. We measure the
fraction of paragraphs in firms’ 10-K that contain at least one word from each
of the following two lists {patent*, patented} and {infringement, infringe}, and
take the average in each industry and year. Third, we follow Acemoglu et al.
(2010) and consider the number of firms active in each firm’s TNIC-3 industry,
and we propose that overall holdup risk is lower in industries populated by
more firms.
Confirming the importance of holdup risk for our interpretation, Columns
4 to 6 of Table 9 indicate that the positive link between firms’ patenting and
vertical acquisitions is significantly smaller when the risk of holdup is lower. In
particular, the negative coefficients on the interaction between P atent/assets
and H I GH indicate that firms’ patenting intensity is less important for vertical
acquisitions in industries displaying an active secondary market for patent
sales and licensing, industries where patent infringement is more common,
and industries featuring many firms.
5. Alternative Explanations
Our results are consistent with the hypothesis that firms’ vertical boundaries are
sensitive to the stage of firms’ innovation through the channels of innovation
incentives and holdup risk. We recognize, however, that even our more refined
33 We thank Don Bowen for sharing his Compustat-merged data on patent transactions from the PAD database.
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results based on contracting, incentive and holdup measures do not fully rule
out alternative explanations. To further explore robustness, we consider two
specific alternatives.
First, the link between the stage of firms’ innovation and vertical acquisitions
could reflect a signaling hypothesis in which vertical buyers view patent grants
as signals of innovative efficiency, thus triggering acquisitions. Second, our
findings could be explained by “reverse-causality” as firms might respond to
the likelihood of vertical acquisitions by simultaneously reducing their R&D
intensity and increasing patents. We consider tests that limit the scope for these
alternative explanations and reinforce our interpretation.
5.1 Innovation efficiency
First, we directly test whether our findings could reflect a link between vertical
acquisitions and firms’ innovative efficiency.34 Under this hypothesis, vertical
acquisitions are unrelated to the trade-off between ex ante incentives and ex
post holdup risk, but reflect the fact that firms that produce more patents per
unit of R&D expense are more likely to get acquired in vertical transactions,
as potential buyers rely on patent grants as signals for efficiency in innovation
activities. In that case, the contrasting effect of firms’ R&D and patenting
intensity on vertical acquisitions should be subsumed (or greatly weakened)
when we control for firms’ innovative efficiency.
To assess this possibility, we include proxies for innovation efficiency in
our main specification. We follow Hirshleifer, Hsu, and Li (2013) and define
innovative efficiency as the ratio of firms’ patents per unit of R&D capital.
Specifically, we scale the number of patents granted to firm i in year t by
its 5-year (or 3-year) cumulated and depreciated R&D expenses (assuming a
depreciation rate of 20%), computed at t −2.35 The first four columns of Table
10 present the results. For firm-year observations without any patent grants, we
either replace missing values by zero in Columns 1 and 2, or exclude them from
the sample in Columns 3 and 4. Across all specifications, we observe that the
opposite effects of firms’ R&D and patenting intensity on vertical acquisition
odds are not affected by the inclusion of firms’ innovative efficiency.
5.2 Nonvertical acquisitions
Second, we examine the link between the stage of development of innovation
and nonvertical acquisitions. We focus on nonvertical transactions as control
transactions since the issues of ex ante incentives, contracting difficulties,
and potential holdup risk should be more specific to deals occurring among
vertically related firms. For instance, unlike our prediction, theories of
34 We note that waiting for the exact degree of efficiency can be consistent with our results as it may be too costly
ex ante to fully contractually specify the degree of innovation efficiency and the effort needed to achieve that
efficiency.
35 That is, P atents /(R&D
t
t−2 +0.8R&Dt−3 +0.6R&Dt−4 +0.4R&Dt−5 +0.2R&Dt−6 ).
30
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Innovation Activities and Integration through Vertical Acquisitions
Table 10
Innovation efficiency and nonvertical acquisitions
Dependent variable:
Specification:
R&D/sales
Patents/assets
Main
(3)
Main
(4)
Nonvert.
(5)
Horiz.
(6)
−0.092∗∗∗
(0.03)
0.111∗∗∗
(0.01)
0.002
(0.01)
−0.088∗∗∗
(0.03)
0.102∗∗∗
(0.01)
−0.162∗∗∗
(0.06)
0.079∗∗∗
(0.02)
−0.036
(0.02)
−0.095∗∗∗
(0.03)
0.101∗∗∗
(0.01)
0.069∗∗∗
(0.02)
0.002
(0.01)
0.090∗∗∗
(0.02)
0.002
(0.01)
Innov. efficiency (3yrs)
Controls
Year FE
#Obs.
Pseudo. R 2
Prob(target)
Main
(2)
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Innov. efficiency (5yrs)
Prob(Vertical target)
Main
(1)
−0.018
(0.02)
0.002
(0.01)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
61,463
0.129
61,463
0.129
21,768
0.139
25,187
0.146
61,463
0.050
61,463
0.061
This table presents results from probit models in which the dependent variable is a dummy indicating whether the
given firm is a target in a vertical (or nonvertical) transaction in a given year. Vertical transactions are identified
using the Vertical Text-10% network. The first four columns focus on vertical transactions and augment the
baseline specification (Column 2 of Table 7) with controls for the efficiency of firms’ innovation, measured over
the last 5 or 3 years. In Column 5), the dependent variable is a dummy indicating whether the given firm is a
target in a nonvertical transaction in a given year (identified using the Vertical Text-10% network.) In Column
6, dependent variable is a dummy indicating whether the given firm is a target in a horizontal transaction in a
given year (identified using the TNIC-3 network). Both specifications include the same independent variable as
the baseline specification (Column 2 of Table 7). For brevity, we only report the coefficients on firms’ R&D and
patenting intensity and their innovation efficiency. Appendix defines the variables. The independent variables
are standardized for convenience. All estimations include year fixed effects. Standard errors are clustered by
FIC-300 industry and year and are reported in parentheses. ∗ p > .1; ∗∗ p > .05; ∗∗∗ p > .01.
acquisitions based on horizontal patent races predict that R&D-intensive
firms have higher incentives to merge in order to internalize the effect of
competition.36
Column 5 of Table 10 present results of regressions in which the dependent
variable is a dummy indicating whether a given firm is a target in a nonvertical
transaction (acquirer-target pair is not in our vertical relatedness network) in
the given year. Similar to Bena and Li (2014), and in sharp contrast to our
results for vertical acquisitions, R&D-intensive firms are significantly more
likely to become targets in nonvertical acquisitions. Mirroring the results in
Bena and Li (2014), patenting intensity is not significantly related to nonvertical
acquisitions. We obtain similar results in Column 6 for horizontal acquisitions
(the acquirer and target are TNIC-3 industry rivals).
5.3 R&D and patenting intensity around acquisitions
The reverse causality hypothesis posits that firms decrease R&D and increase
patents prior to an acquisition, perhaps to attract more favorable terms. Under
this hypothesis, it is likely that R&D should bounce back after vertical
acquisitions because the motive for its reduction was rent extraction, and
36 For instance Phillips and Zhdanov (2013) predict and show empirically that R&D is positively related to
transaction likelihood for horizontal transactions, as firms wish to internalize their R&D on competing products.
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not maximization of operating value (which would be the objective postacquisition). Our hypothesis based on the theory of the firm, in contrast, predicts
that declines in R&D should be permanent post-acquisition. This permanently
lower R&D further predicts that the increase in patents will be strong only
around the acquisition date, as the reduced R&D eventually reduces patenting.
Contrasting these predictions empirically is challenging, because the
majority of targets cease to exist as separate entities after their acquisition.
Nevertheless, we can precisely track the evolution of R&D intensity around
vertical transactions for a subsample of 731 targets that continue to operate
independently for at least 1 year post-acquisition. We also construct a synthetic
measure of R&D intensity by summing the targets’ and acquirers’ R&D prior to
acquisition, and using the acquirer post-acquisition (e.g., Maksimovic, Phillips,
and Prabhala 2011). With both approaches, we follow targets (and synthetic
entities) from 3 years prior to their acquisitions to 3 years after. To track the
evolution of firms’ R&D and patenting intensity around transactions, we regress
their R&D and patenting intensity on (seven) event-time binary variables
identifying time to acquisitions (from t −3 to t +3).37
Figure 4 plots the estimated event-time coefficients. In panel A, we find that
targets’ patenting activity peaks in the year of the acquisition and decreases
afterward. The results are similar but less pronounced in panel B for synthetic
entities. This evolution supports our hypothesis that vertical deals materializes
when targets’ innovation becomes realized and legally protected, which reduces
holdup risk for acquirers. More importantly, target R&D intensity significantly
declines post-acquisition in both panels, similar to Seru (2014). Although this
evidence is indirect, the permanently lower R&D intensity post-acquisition is
hard to reconcile with the reverse causality alternative. This is also inconsistent
with an alternative story in which buyers purchase the most innovative firms
and maintain their innovation incentives through internal contracting. Yet
the observed decline in R&D intensity supports the idea that vertical deals
take place between firms when preserving incentives for further development
becomes relatively less important compared to owning property rights.
Overall, we present a large body of evidence and tests consistent with the
idea that vertical transactions arise as an optimal trade-off between the need
to mitigate holdup risk and preserve investment incentives. We acknowledge
however that our evidence remains largely indirect due to the difficulty to
observe and measure firms’ incentives. In particular, we recognize that part of
our results might arise because some acquirers use patent grants as indications
for targets’ genuine success (and not their innovation efficiency), triggering
their acquisitions. This explanation can be distinct from our explanation if
no ex ante incentives are needed for the effort needed to achieve success.
Although various tests (especially our subsample tests based on proxies for
37 Specifications include year and firm fixed effects. We require that each firm has at least one available observation
before and after the deal.
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Innovation Activities and Integration through Vertical Acquisitions
.013
.04
.014
(A) Vertical target stand-alone
.01
.008
.009
.01
.02
R&D/sale
.03
Patents/assets
.011
.012
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t-3
t-2
t-1
t
t+1
time to acquisition
t+2
t+3
t-3
t-2
t-1
t
t+1
time to acquisition
t+2
t+3
t-2
t-1
t
t+1
time to acquisition
t+2
t+3
.01
.007
.02
.008
R&D/sale
Patents/assets
.03
.009
.01
.04
(B) Vertical synthetic entity
t-3
t-2
t-1
t
t+1
time to acquisition
t+2
t+3
t-3
Figure 4
R&D and patenting intensities around vertical acquisitions.
This figure plots the evolution of targets’ R&D and patenting intensity from 3 years prior to their vertical
acquisitions to 3 years after. Panel A includes 731 targets that continue to operate independently for at least 1
year post-acquisition. In panel B, we construct a synthetic measure of R&D and patenting intensity by summing
the targets’ and acquirers’ R&D and patents prior to acquisition, and using the acquirer post-acquisition. We track
the evolution of firms’ R&D and patenting intensity around transactions by regressing their R&D and patenting
intensity on seven event-time binary variables identifying time to acquisitions (from t −3 to t +3) and report the
estimated coefficients. We require that each firm has at least one available observation before and after the deal.
Specifications include year and firm fixed effects.
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incentive provision and holdup risk) limit support for explanations unrelated to
incentives and holdup (such as the signaling of quality), we cannot fully rule
out such alternatives.
6. Vertical Integration within Firms
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To further examine the link between the stage of development of innovation
and firms’ vertical boundaries we also consider the degree of firms’ vertical
integration. While our central prediction is about the role of unrealized and
realized innovation in vertical acquisitions (i.e., a reallocation of control rights
between firms), the importance of ex ante innovation incentives and ex post
holdup risk also should be prevalent in explaining variation in firms’ vertical
integration.
We define the extent to which a given firm i is “vertically integrated”
(V Ii ) using the diagonal entries of the matrix U P that we use to measure
vertical relatedness between firms (see Equation (1) in Section 2.2.3). With that
measure, a firm displays a higher degree of vertical integration if its business
description contains many word pairs that are vertically related, that is, when its
product vocabulary spans vertically related markets.38 In the Internet Appendix,
we present various evidence indicating that V I truly captures the extent to
which firms are vertically integrated (e.g., correlation with mentions of vertical
integration in firms’ 10-Ks).
We first examine whether the degree of firms’ vertical integration varies
systematically across industries based on their respective innovative maturity.
Our hypothesis predicts that vertical separation is preferred to integration
when incentives for further innovation are important. Thus, industries in which
innovation is mostly unrealized should feature independent (i.e., separate) firms
displaying low levels of vertical integration, as these specialized firms will be
integrated into other firms (via acquisitions) when their innovation is realized
and patented. In contrast, industries where innovation is mostly realized should
feature firms that display higher levels of vertical integration, resulting from
their vertical acquisitions.39
We assess these predictions by regressing firms’ vertical integration on the
average R&D and patenting intensity of their industry as well as (industrylevel) controls variables that are similar to those used in our main acquisition
specification (see Table 7). The first column of Table 11 reveals that firms in
R&D-intensive industries are less vertically integrated, and those in patenting
38 Unfortunately, data limitations prevent us from determining the economic weight of each product from firms’ 10-
K product descriptions. Thus, while V I is a novel measure that uniquely captures firm-level vertical integration,
it cannot account for product-by-product importance.
39 We do find that transactions classified as vertical are followed by an increase in our firm-level measure of vertical
integration (V I ). Using the Vertical Text-10% network, acquirers in vertical transactions experience a 12%
increase in V I 1 year after the acquisition. In contrast, acquirers in nonvertical transactions experience an 8%
decrease in V I .
34
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Innovation Activities and Integration through Vertical Acquisitions
Table 11
Determinants of vertical integration
(Text-based) VI
Dep. variable:
Ind.(R&D/sale)
Full sample
(1)
(2)
(3)
(4)
−0.003
(0.003)
0.007∗∗
(0.003)
−0.006
(0.009)
0.035∗∗∗
(0.012)
−0.001
(0.004)
0.001
(0.002)
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
61,463
.586
60,463
.849
11,328
.751
49,135
.859
−0.076∗∗∗
(0.006)
0.031∗∗∗
(0.006)
Firm R&D/sale
Firm patents/assets
Controls
Year FE
Industry FE
Firm FE
#obs.
Adj. R 2
Never did a
vertical
acquisition
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Ind.(Patents/assets)
Full sample
At least one
vertical
acquisition
This table presents results from OLS models in which the dependent variable is our firm-level measure of vertical
integration V I . In all models, the control variables are similar to the baseline specification (Column 2 of Table 7).
In Column 1 R&D and patenting intensities are computed at the industry-level, based on TNIC-3 industries, and
the specification include year and industry fixed effects (defined using FIC-100 industries from Hoberg and
Phillips (2016)). In Columns 2 to 4, we consider firms’ R&D and patenting intensity, and specifications include
year and firm fixed effects. Column 3 only includes firms that did at least one vertical acquisition during our
sample period. Column 4 includes the complementary subsample: firms that did not do any vertical acquisitions
during our sample. For brevity, we only report the coefficients on industry and firm R&D and patenting intensity.
Appendix defines the variables. The independent variables are standardized for convenience. Standard errors are
clustered by FIC industry and year and are reported in parentheses. ∗ p > .1; ∗∗ p > .05; ∗∗∗ p > .01.
intensive industries are more integrated. These results confirms the importance
of innovative maturity in explaining variation in integration across firms.
In Column 2 of Table 11 we examine how the variation of vertical integration
within firms (over time) relates to their R&D and patenting intensity (through
the inclusion of firm fixed effects in the specification). We report that changes
in firms’ degree of vertical integration is largely insensitive to their R&D
intensity, consistent with our hypothesis which predicts that firms should remain
independent and nonintegrated as long their innovation remains unrealized.
In contrast, we observe a positive and significant link between firms’ extent
of vertical integration and their patenting intensity. Columns 4 and 5 reveal,
however, that the positive relation between patenting intensity and integration
is only present for firms that did acquire vertically related firms, suggesting
that the positive integration-patent relationship stems from the patents obtained
through vertical deals. Column 5 shows that the degree of vertical integration
of firms not participating in vertical acquisitions is largely insensitive to the
stage of development of innovation. Taken together, the observed variation in
vertical integration within firms is consistent with the trade-off between ex ante
innovation incentives and ex post holdup risk being at play to explain changes
in vertical boundaries between firms, but not within firms.
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7. Conclusions
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Our paper examines the link between the stage of development of firms’
innovation and vertical acquisitions. We consider theoretical predictions
regarding how incentives to invest in R&D, and the potential for ex post holdup
influence the incidence of vertical transactions. We develop a new measure
vertical relatedness between firm pairs using computational linguistics analysis
of firm product descriptions and how they relate to product vocabularies from
the BEA Input-Output tables. The result is a dynamic network of vertical
relatedness between publicly-listed firms, enabling us to precisely identify
takeover transactions occurring between vertically related firms.
We find that unrealized innovation (R&D) and realized innovation (patents)
are related to vertical acquisitions in opposite ways. R&D-intensive firms
are less likely to be purchased by vertically related buyers, consistent with
separation maintaining ex ante incentives to invest in intangible capital and to
maintain residual rights of control, as in the property rights theory of Grossman,
Hart, and Moore. In contrast, patenting intensive firms with high realized
innovation are significantly more likely to be acquired by a vertical buyer, in
line with legally enforceable residual rights of control reducing holdup riskthat
may can occur with commercialization and bringing new products to market,
as in contracting costs literature of Williamson.
Appendix: Variable Descriptions
In this appendix, we describe the variables used in the analysis. We report
Compustat items in parentheses when applicable. All ratios are winsorized at
the 1% level in each tail.
New Data from Text Analysis
• V I10k is a binary variable indicating whether a firm mentions the terms
“vertical integration” and “vertically integrated” in its 10-K (excluding
cases in which the firm indicates that lacks integration).
• VI measures the degree to which a firm offers products and services that
are vertically related based on our new text-based approach to measure
vertical relatedness (as defined in Section 6).
• End user measures the degree to which a firm-year’s products exit the
U.S. supply chain. We characterize whether a firm supplies products or
services that exit the supply chain using the exit correspondence matrix
E, which includes industries present in the Use table, but not in the Make
table (retail customers, the government, and exports). E is a one-column
vector containing the fraction of each IO commodity that flows to these
final users. We compute End U seri as [B ·E]i (in the [0,1] interval)
to compute the cosine similarity between the text in a firm’s business
description and the text in IO commodities that exit the supply chain. A
higher value of End U seri indicates that firm i has a higher fraction of
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its products and services that are sold to retail, the government, or foreign
entities.
• Employee incentives measures the fraction of paragraphs in a firm’s
10-K that contain at least one word from each of the following two
lists: {employee*} and {contributions, retention, incentive, award*,
equity, option, grant, deferred, vest*, stock, compens*, plan*, benefit*,
shares}.40
• Keyman insurance measures the fraction of paragraphs in a firm’s 10-Ks
that contain the bigram “key man” (excluding “do not”).
• Patent infringement measures the fraction of paragraphs that contain at
least one word from each of the following two lists. List 1 is {patent,
patents, patented }. List 2 is {infringement, infringe}.
Data from Existing Literature
• R&D/sales is equal to research & development expenses (XRD) scaled
by the level of sales (SALE). We replace missing values by the median
value of firms’ industry (based in TNIC-3 industries).
• Patents/assets is the number of patents granted in a given year scaled by
the level of assets (AT). Patents granted by the U.S. Patent and Trademark
Office (USPTO) between 1996 and 2010 are obtained by combining
information from the NBER patent data set and the recent data compiled
by Kogan et al. (2017). We extend these data by manually matching
all patent grants to firms in our sample between 2011 and 2013 using
assignee names.
• PPE/assets is equal to the level of property, plant, and equipment
(PPENT) divided by total assets (AT).
• HHI measures the degree of concentration (of sales) within TNIC-3
industries. We compute HHI as the TNIC-3 HHI in Hoberg and Phillips
(2016), which is based on the text-based TNIC-3 horizontal industry
network.
• log(assets) is the natural logarithm of the firm assets (AT).
• log(age) is the natural logarithm of one plus the firm age. Age is computed
as the current year minus the firm’s founding date. When we cannot
identify a firm’s founding date, we use its listing vintage (based on the
first year the firm appears in the Compustat database).
• Segments is the number of operating segments observed for the given
firm in the Compustat segment database. We measure operating segments
based on the NAICS classification.
• MB is the firm’s market-to-book ratio. It is computed as total assets
(TA) minus common equity (CEQ) plus the market value of equity
((CSHO×PRCC_F)) divided by total assets.
40 ‘*’ indicates a wild card.
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• CF/assets is equal to income before extraordinary items (IB) plus
depreciation and amortization (DP), scaled by total assets (AT)
• OI/sales is equal to operating income before depreciation (OIBDP)
divided by sales (SALE).
• Sales growth is equal to annual growth rate of sales (SALE).
• Inventor mobility is the fraction of inventors listed on patents granted
to a firm in a given year who move (i.e., listed on patents granted) to
another firm in the next 5 years (from the PatentView initiative). We
restrict attention to inventors who receive at least one patent grant with
a public firm between 1990 and 2013, and exclude moves between firms
due to mergers and acquisitions, based on transactions data from SDC.
• Patent liquidity is the annual fraction of public firms’ existing patents that
are sold or licensed (based on the USPTO Patent Assignment Database
(PAD)) to other public firms between 1996 and 2013, averaged over each
firm’s industry (based on TNIC-3 industries).
• Peers measures the number of firms in each firm’s TNIC-3 horizontal
network.
• Innovation efficiency is equal the number of patents granted to given firm
in year t divided by its 5-year (or 3-year) cumulated and depreciated
R&D expenses (assuming a depreciation rate of 20%), computed at t −2
as R&Dt−2 +0.8R&Dt−3 +0.6R&Dt−4 +0.4R&Dt−5 +0.2R&Dt−6 .
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